# uamcf/utils/architecture_generator.py
import torch
import torch.nn as nn
import logging
from typing import Dict, List, Optional, Any

logger = logging.getLogger("ArchitectureGenerator")


class ArchitectureGenerator:
    """根据权重字典自动生成模型架构"""

    def __init__(self):
        pass

    def generate_from_state_dict(self, state_dict: Dict) -> Optional[nn.Module]:
        """从状态字典生成模型架构"""
        try:
            # 分析层结构
            layers = self._analyze_layer_structure(state_dict)

            # 构建动态模型类
            model_class = self._build_model_class(layers)

            # 实例化模型
            model = model_class()

            # 加载权重
            model.load_state_dict(state_dict)

            return model

        except Exception as e:
            logger.error(f"生成架构失败: {str(e)}")
            return None

    def _analyze_layer_structure(self, state_dict: Dict) -> List[Dict]:
        """分析状态字典中的层结构"""
        layers = []

        # 分析各层形状和连接方式
        # 这是一个复杂过程，需要考虑不同层的命名规则
        # 以下是示例逻辑

        weight_keys = [k for k in state_dict.keys() if 'weight' in k]

        for key in weight_keys:
            if 'conv' in key:
                # 分析卷积层
                weight = state_dict[key]
                bias_key = key.replace('weight', 'bias')
                has_bias = bias_key in state_dict

                # 确定卷积层参数
                out_channels, in_channels = weight.shape[0], weight.shape[1]
                kernel_size = weight.shape[2] if len(weight.shape) > 2 else 1

                layers.append({
                    'name': key.rsplit('.', 1)[0],
                    'type': 'conv2d' if len(weight.shape) == 4 else 'conv1d',
                    'in_channels': in_channels,
                    'out_channels': out_channels,
                    'kernel_size': kernel_size,
                    'has_bias': has_bias
                })

            elif 'fc' in key or 'linear' in key:
                # 分析全连接层
                weight = state_dict[key]
                bias_key = key.replace('weight', 'bias')
                has_bias = bias_key in state_dict

                # 确定全连接层参数
                out_features, in_features = weight.shape

                layers.append({
                    'name': key.rsplit('.', 1)[0],
                    'type': 'linear',
                    'in_features': in_features,
                    'out_features': out_features,
                    'has_bias': has_bias
                })

            # 添加其他层类型的分析逻辑...

        # 推断层之间的连接
        self._infer_connections(layers)

        return layers

    def _infer_connections(self, layers: List[Dict]) -> None:
        """推断层之间的连接关系"""
        # 这需要基于层名称和预期的数据流来实现
        # 例如，通常名称为'layer1.0.conv1'的层会连接到'layer1.0.conv2'

        # 简单实现：假设层按顺序连接
        for i in range(len(layers) - 1):
            layers[i]['next'] = layers[i + 1]['name']

    def _build_model_class(self, layers: List[Dict]) -> Any:
        """基于分析的层构建动态模型类"""

        class DynamicModel(nn.Module):
            """动态生成的模型架构"""

            def __init__(self):
                super().__init__()
                self.layers = nn.ModuleDict()
                self.execution_order = []

                # 创建各层
                for layer_info in layers:
                    name = layer_info['name']
                    layer_type = layer_info['type']

                    if layer_type == 'conv2d':
                        layer = nn.Conv2d(
                            layer_info['in_channels'],
                            layer_info['out_channels'],
                            kernel_size=layer_info['kernel_size'],
                            bias=layer_info['has_bias']
                        )
                    elif layer_type == 'conv1d':
                        layer = nn.Conv1d(
                            layer_info['in_channels'],
                            layer_info['out_channels'],
                            kernel_size=layer_info['kernel_size'],
                            bias=layer_info['has_bias']
                        )
                    elif layer_type == 'linear':
                        layer = nn.Linear(
                            layer_info['in_features'],
                            layer_info['out_features'],
                            bias=layer_info['has_bias']
                        )
                    # 添加其他层类型...

                    self.layers[name] = layer
                    self.execution_order.append(name)

            def forward(self, x):
                # 简单的前向传播实现
                for name in self.execution_order:
                    layer = self.layers[name]
                    x = layer(x)

                return x

        return DynamicModel